However, style transfer has value beyond this. Representational artwork (i.e. artwork depicting scenes with recognizable semantics) offers a unique window into the human visual system. It is a byproduct of our internal representations of content which artists craft to be both recognizable and visually stimulating. Artwork frequently presents visual stimuli which we are unlikely to encounter in the natural world, and which test the boundaries of our perceptual system. Style transfer can be used to leverage these exciting properties of art in two ways. One is that improving style transfer motivates developing an explicit understanding of 'content' and it's invariances. In the future these insights could be used to develop better automatic recognition algorithms. The second, more direct, path is using style transfer as a black box to augment training data, producing many stylizations of labeled content images to aid generalization [1]}, [2]}. Both paths are served by developing algorithms that can more closely match the abilities of human artists.
